Qdrant

Qdrant develops and operates Qdrant, an open-source vector database built entirely in Rust for similarity and semantic search. A vector database stores content as numeric vectors, letting it find similar meanings rather than only exact word matches. Qdrant also provides the official Qdrant MCP Server.

The platform supports hybrid search, combining dense and sparse vectors (including BM25, SPLADE++, and miniCOIL) in a single query, along with advanced metadata filtering using nested, text-based, and geo filters. Multivector support allows multiple vectors per object for improved relevance and multimodal retrieval, while single-stage filtering during HNSW traversal enables high recall at low latency. For fine-tuning results, the platform offers reranking via score boosting, late-interaction models (ColBERT), and maximum marginal relevance; quantization reduces memory usage by up to 64x, according to the company.

The business model combines the open-source database (30,000+ GitHub stars) with managed cloud offerings on AWS, GCP, and Azure, complemented by hybrid and private cloud variants and an experimental edge version for decentralized deployments. Enterprise customers additionally get single sign-on, role-based access control, and SOC 2, HIPAA, and GDPR certifications.

Typical use cases include RAG systems, AI agents, semantic search, recommendation engines, and anomaly detection; customers include TripAdvisor, OpenTable, and HubSpot.

Qdrant was founded in Berlin in 2021 by Andre Zayarni and Andrey Vasnetsov, who serve as CEO and CTO respectively. The decision to implement the database in Rust rather than a language like Python was aimed at performance and memory safety from the start — one reason Qdrant is also used for very large, latency-sensitive deployments.